Empowering research in chemistry and materials science through intelligent algorithms

In this review, we explore the integration of intelligent algorithms in chemistry and materials science.We begin by delineating the core principles of Machine Learning, Deep Learning, and optimization algorithms, highlighting their bespoke adaptation to these scientific domains. The focus then shift...

Full description

Saved in:

Bibliographic Details
Published in:	Artificial intelligence chemistry Vol. 2; no. 1; p. 100035
Main Authors:	Lin, Jinglong, Mo, Fanyang
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.06.2024 Elsevier
Subjects:	Deep Learning Intelligent Algorithms Machine Learning Intelligent Algorithms Machine Learning Deep Learning
ISSN:	2949-7477, 2949-7477
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	In this review, we explore the integration of intelligent algorithms in chemistry and materials science.We begin by delineating the core principles of Machine Learning, Deep Learning, and optimization algorithms, highlighting their bespoke adaptation to these scientific domains. The focus then shifts to the critical processes of data management, including collection, refinement, and feature engineering, alongside strategies for efficient data mining from targeted databases and literatures. Subsequently, we present a concise overview of the diverse applications of these algorithms, emphasizing their transformative impact in both fields. Finally, this review explores the future prospects and challenges of these emerging algorithms.
ISSN:	2949-7477 2949-7477
DOI:	10.1016/j.aichem.2023.100035